Even before the introduction of practically usable quantum hardware, research developed algorithms for quantum computers for trading, which are theoretically considered and evaluated based on runtime analysis compared to classical approaches. Since quantum computers are available with an acceptable number of qubits, very few works aim at a practical evaluation of these algorithms with real-time data. We want to evaluate an approach for high-frequency trading with statistical arbitrage based on practical runtime and profitability, which we ran completely on a classical computer as well as using quantum computing. To compare the profitability with a Machine Learning (ML) algorithm, we implemented a support vector classifier and tested and evaluated it practically. We present various functions used for the algorithms and describe the design and implementation decisions. Previous research, which has looked at the algorithms theoretically or on past data, has demonstrated very good profitability. However, this profitability is only partially or not at all identifiable in the practical usage due to this work. We found out that the Statistical Arbitrage Algorithm we run exclusively on the classical computer provides significantly faster processing of the data than the execution on the quantum simulator. We saw in the experiment, that the speed of the quantum-based algorithm was significantly slower than the classical algorithm. In terms of profitability, the ML algorithm can be attributed the greatest benefit.